Amazon EventBridge API destinations now support proactive OAuth token refresh for public and private OAuth authorization endpoints. This capability helps you to preemptively mitigate delays and errors during invocations when the OAuth access token is close to expiration.
EventBridge API destinations are HTTPS endpoints that you can invoke as the target of an event bus rule, or pipe, similar to how you invoke an AWS service or resource as a target. Using API destinations, you can route events between AWS services, integrated software as a service (SaaS) applications, and public or private applications by using API calls. Now, when you invoke an API destination with an OAuth token that is close to expiration, EventBridge asynchronously requests a new token from your OAuth endpoint, reducing authorization errors and delays due to expired tokens.
Amazon EventBridge API destinations proactive OAuth token refresh is available at no additional cost across all AWS Regions where EventBridge API destinations is available. Please refer to the API destinations user guide for details.
Amazon Connect provides a refreshed experience for configuring hierarchies on the admin website, giving customers better ways to navigate complex organizational structures quickly and accurately. Hierarchies are a way for customers to organize their agents into teams and groups for reporting purposes (e.g. by department, location, or skill sets). Now customers can visualize their tree structure and find resources using free-text type-ahead search. Because this UI is now powered by public APIs, customers can leverage AWS CloudTrail to log, view and audit all changes to hierarchy groups and structures regardless of who made them or how.
The updated UI with AWS CloudTrail support is available in all AWS Regions where Amazon Connect is offered. For more information, see Viewing Events with CloudTrail event history. To learn more about Amazon Connect, the AWS contact center as a service solution on the cloud, please visit the Amazon Connect website.
Amazon Connect now records all changes made on the Agent status page as events in AWS CloudTrail. This enables you to look in AWS CloudTrail to identify which admin website user added, updated or disabled an agent status, for example adding ‘Break’ and ‘Training’ to the standard available and offline choices. Now you have the same logging capabilities available on other resources such as users, queues, and flows that help you analyze and monitor changes to your contact center.
The updated UI with AWS CloudTrail support is available in all AWS Regions where Amazon Connect is offered. For more information, see Viewing Events with CloudTrail event history. To learn more about Amazon Connect, the AWS contact center as a service solution on the cloud, please visit the Amazon Connect website.
If you’re a regular reader of this blog, you know that 2024 was a busy year for Google Cloud. From AI to Zero Trust, and everything in between, here’s a chronological recap of our top blogs of 2024, according to readership. You’ll probably see some favorites on this list, and discover a few that you missed the first time around.
January
We started the new year strong, removing data transfer fees for anyone moving data off of our platform. Translation: Anyone doing cool things on Google Cloud (like using generative AI to analyze their microservices deployment) is doing it because they want to, not because they have to. And in business news, we shared how to make the most out of your data and AI in the coming year.
From local GPUs, to model libraries, to distributed system design, the second month of 2024 was the first of many to come where AI topics dominated the charts. Our Transform team explored gen AI’s impact on various industries.
If it wasn’t already obvious, this month’s top-read blogs showed that our core audience is developers pushing the boundaries of innovation. Business leaders, meanwhile, read about best practices for securely deploying AI on Google Cloud.
Watch out, here comes Google Cloud Next, where we made a record 218 announcements; the top three are listed here. Readers were also keen to hear about how Citadel Securities built out a research platform on Google Cloud.
We don’t always get it right, but when there’s a problem, we’re committed to providing you with accurate, timely information with which to make your own assessments. We’re also committed to making you, and customers like McLaren Racing, go really, really fast when developing new AI-based applications.
Whether you wanted to modernize your databases, deliver higher system reliability, create really cool AI-powered apps, or learn how legendary companies tackle data management, the Google Cloud blog was your go-to source midway through the year.
We talk a lot about “meeting customers where they are.” Sometimes that means a disaster zone, a remote research station, or a truck cruising down the highway. Over on Transform, you read about the history of our custom Axion and TPU chips.
Just when you thought you knew how to run AI inference, your available graph database options, or the name of Google Cloud’s event-driven programming service, we went and changed things up. We like to keep you on your toes 😉 And business readers got a first look at AI agents — more to come on this.
You’ve been generating (and storing) business data for years. Now, we’re making it easier for you to make sense of, and actually use, that data. Speaking of using data, the Transform team compiled a jaw-dropping list of the real-world ways customers are using gen AI in their organizations.
According to this month’s most-read blog, 75% of you rely on AI for at least one daily professional responsibility, including code writing, information summarization, and code explanation, and experience “moderate” to “extreme” productivity gains. So it was no big surprise that business leaders wanted to read about how to develop an AI strategy.
Not content to hold the existing record for most nodes in a Kubernetes cluster (15,000), we went ahead and more than quadrupled it, to the delight of AI unicorns. But whether you work for an AI unicorn, or just a plain old zebra, all Google Cloud users needs to start using multi-factor authentication next year, as well as learn how to avoid comman AI pitfalls.
We’re closing out the year on an AI highnote, with the availability of amazing new image and video generation models, as well as the new Trillium TPU, which Google used to train Gemini 2.0, our most capable AI model… yet. Be on the lookout for how these technologies — and many others — will reshape your industry and how you work in the coming year.
Spanner is Google’s always-on, virtually unlimited database that powers planet-scale applications like Gmail, YouTube, and Google Photos. Outside of Google, Spanner powers demanding workloads for household brands like Yahoo!, The Home Depot, Wayfair, and Pokémon Go. Today, Spanner handles over 4 billion queries per second at peak and more than 15 exabytes of data, with five 9s of availability, plus global consistency.
Since we first discussed it in 2012, Spanner has evolved from a groundbreaking distributed SQL database into a versatile, intelligent innovation platform. 2024 was a big year for Spanner, with multiple launches that expanded its functional capabilities, pushed the envelope on price-performance, re-architected it for best-in-class reliability and security, and enhanced the developer experience. Here is a recap of Spanner’s biggest innovations of the year and how you can benefit from them.
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1. Multi-model — one database, many possibilities
With the launch of Spanner Graph, full-text search and vector search, Spanner went from being a highly available, globally consistent and scalable database, to a multi-model database with intelligent, interoperable capabilities with which you can build AI-enabled applications. Unlike other multi-model databases on the market, Spanner offers a true multi-model experience that allows interoperability between different data models without downtime.
Spanner’s multi-model support lets you consolidate databases, saving on costs and reducing operational overhead, governance, and security touchpoints, while its interoperability eliminates data movement for a “true ZeroETL” experience with consistent data across all models. This helps enable use cases like near-real-time fraud detection, supply chain optimization, or product recommendations.
Fig1: A SQL query on Spanner showing interleaved usage of graph, relational, and vector models and full-text search
2. Improving price-performance
Spanner’s price-performance lets you dream big, start small (for as little as $65/mo), and scale linearly with no cliffs. In 2022, we increased the storage per node from 2T to 4T, and in 2023 we built on this with a 50% increase in throughput and a 2.5X increase in storage at no additional cost.
This year, with the launch of new multi-model capabilities, we wanted to make it simple and cost effective for you to use these capabilities without charging incrementally for every new feature. The result was Spanner editions, an intuitive, tier-based pricing approach that offers different capabilities at various price points to fit you diverse needs and budgets, all while providing flexibility, cost transparency and additional cost saving opportunities.
3. A new home for your Cassandra workloads
The Cassandra NoSQL database is prized for its speed and scalability. It also has limitations, such as limited support for complex queries and difficulty modeling intricate relationships. Spanner combines the scalability and availability of NoSQL with the strong consistency and relational model of traditional databases, for the best of both worlds.
This year, we launched the Cassandra to Spanner Proxy Adapter, an open-source, plug-and-play tool that makes it easier than ever to move your Cassandra workload to Spanner with near-zero changes to your application logic. Customers like Yahoo! and Reltio are loving the ease of use of the Cassandra proxy adapter, and we’re excited to help customers be more successful with Cassandra on Spanner.
4. Generative AI and the Spanner ecosystem
Over the past year, we’ve witnessed a remarkable shift in how organizations leverage generative AI. But gen AI comes with risk of hallucinations. We believe thattransactional and analytical databases can help reduce these,bridging the gap between foundation models and enterprise gen AI apps. Here’s how:
Vector support: With vector support for Spanner, developers can perform similarity searches on vector embeddings stored in the database. Spanner vector search supports both exact KNN and approximate ANN searches, providing flexibilit for different workloads that leverage Google’s scalable nearest neighbor (ScaNN) algorithm, providing fast and accurate results, even on large datasets. Spanner now supports vector searches scaling to more than 10 billion vectors. Developers can combine vector searches with regular SQL and graph GQL queries to power use-cases like RAG applications.
BigQuery and Spanner better together: New, groundbreaking integrations between Spanner and BigQuery help businesses connect operational and analytical workloads, to unlock valuable insights and drive better decision-making. Spanner external datasets in BigQueryallows you to query transactional data residing in Spanner directly within BigQuery, without needing to move or duplicate data. Spanner now also supports reverse ETL from BigQuery to export data from BigQuery to Spanner, so you can operationalize the analytical insights that BigQuery enables.
5. Reliability, availability, security, and governance
Spanner customers expect the highest levels of reliability, availability, security, and governance controls for their mission-critical workloads. This year, we launched support for dual-region configurations and geo-partitioning to help you improve your availability SLAs, improve application performance for multi-region workloads, and meet governance requirements.
Dual-region support: Spanner dual-region configurations help meet local residency requirements while providing five 9s of availability and zero recovery-point objective (RPO) guarantees in geographies with only two regions.
Geo-partitioning: You can partition your table data at the row-level across the globe, to serve data closer to your users. With geo-partitioning, Spanner customers across industries like gaming, e-commerce, and financial services can provide theirusers reduced application latency, optimized costs, and data residency benefits such as storing sensitive user data within geographic jurisdictions.
At Google Cloud, we strive to make it ridiculously simple to build and manage applications built on our databases, including Spanner.
Protobuf improvements:Protocol Buffers, or protobuf, is a language-neutral way to encode and decode data structures for efficient transport and storage. You can now manage protobuf values in Spanner and access their fieldsusing the dot operator in SQL, e.g., dimensions.size.width, without having to normalize into tables upfront. This dramatically simplifies writing queries that need to filter, group, or order by specific values within a protobuf.
Troubleshooting and Database Center support: Database Center is an AI-powered unified database management solution to monitor and manage diverse database services. This year, customers started to be able to use Database Center to manage their Spanner databases. We also added support for end-to-end tracing and client tracing to make it easier to troubleshoot performance issues.
We are proud of what we have delivered for customers in 2024, and are excited to see the innovative solutions you are building on Spanner. Needless to say, we are just getting started and we have a lot more exciting capabilities lined up for 2025.
Get started
Want to learn more about what makes Spanner unique and how it’s being used today? Try it yourself for free for 90-days or for as little as $65 USD/month for a production-ready instance that grows with your business without downtime or disruptive re-architecture.
Google Cloud’s Database Center provides a unified fleet management solution to help manage your databases at scale. In October 2024, Database Center was made available to all Google Cloud customers, with support for Cloud SQL, AlloyDB and Spanner engines.
Today we are expanding Database Center’s capabilities with the addition of Bigtable, Memorystore, and Firestore databases in preview. You now have a single, unified view where you can:
Gain a comprehensive view of your entire database fleet across all Google Cloud managed databases. No more silos of information or hunting through bespoke tools and spreadsheets.
Proactively de-risk your database fleet with intelligent and actionable availability and data protection recommendations for Bigtable and Firestore databases.
Optimize your database fleet with AI-powered assistance using a natural language interface to answer questions about all your Google Cloud databases,and quickly resolve fleet issues through optimized recommendations.
Let’s take a deeper look at the new Database Center capabilities for Bigtable, Memorystore and FIrestore.
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Gain a comprehensive view of your database fleet
Database Center simplifies database management with a single, unified view of all your Google Cloud managed database services, including Bigtable, Memorystore and Firestore. You can monitor these database resources across your entire organization, spanning multiple engines, versions, regions, projects, and environments or applications using labels. Specifically, Database Center now lets you:
Identify out-of-date database versions to ensure proper support and reliability
Track version upgrades, e.g., if Memorystore Redis 6.x to Memorystore Redis 7.0/ 7.2is updating at an expected pace
Ensure database resources are appropriately distributed, e.g., identify the number of Bigtable, Firestore, Memorystore databases powering the critical production applications vs. non-critical dev/test environments
Detecting and troubleshooting diverse database issues across the entire fleet.
Proactively de-risk your database fleet with intelligent recommendations
We’ve expanded Database Center’s proactive monitoring and issue-resolution capabilities to support Bigtable and Firestore, helping to ensure optimal availability and data protection for your existing database fleet. For instance, Database Center:
Proactively monitors Bigtable instances, detecting and helping to resolve failover gaps to minimize downtime and prevent service disruption
Publishes recommendations related to any unsuccessful backup attempts, no automated back-up policies and short back-up retention for your Bigtable instances. It’s important to address these issues quickly to ensure data can be recovered.
Enhances data availability and durability by protecting against system failures and regional outages for your Bigtable instances.
Helps safeguard your critical data in Firestore by detecting if any tables lack an automated backup policy, so you can prevent data loss from accidents or corruption
In short, when issues arise, Database Center guides you through intuitive troubleshooting steps, streamlining resolution and minimizing downtime for your Bigtable and Firestore deployments. It goes beyond problem identification to provide clear, actionable solutions. Recommendations for Memorystore are coming to Database Center soon along with additional recommendations for other engines!
Detecting and troubleshooting diverse database issues across the entire fleet.
Optimize your database fleet with AI-powered assistance
With Gemini enabled, Database Center makes optimizing your database fleet incredibly intuitive. Chat with the AI-powered interface to get precise answers, uncover issues within your database fleet, troubleshoot problems, and implement solutions. AI-powered chat in the Database Center now includes support for Bigtable, Memorystore and Firestore. For example, Gemini can help you quickly identify Firestoreresources that do not have automated back-up policies.
A natural language Interface to ask questions about Bigtable, Memorystore and Firestore databases.
Get started with Database Center today
With today’s launch, Database Center now provides you a single, unified view across all your Google Cloud managed databases. You can access the Database Center within the Google Cloud console and begin monitoring and managing your entire database fleet. To learn more about Database Center’s capabilities, check out the documentation.
Today, AWS Backup announces additional options to assign resources to a backup policy on AWS Organizations in the AWS GovCloud (US) Regions. Customers can now select specific resources by resource type and exclude them based on resource type or tag. They can also use the combination of multiple tags within the same resource selection.
With additional options to select resources, customers can implement flexible backup strategies across their organizations by combining multiple resource types and/or tags. They can also exclude resources they do not want to back up using resource type or tag, optimizing cost on non-critical resources.
To get started, use your AWS Organizations’ management account to create or edit an AWS Backup policy in the AWS GovCloud (US) Regions. Then, create or modify a resource selection using the AWS Organizations’ API, CLI, or JSON editor in either the AWS Organizations or AWS Backup console. For more information, visit our documentation and launch blog.
Amazon Q in Connect now supports 64 languages for agent assistance capabilities. Customer service agents can now chat with Q for assistance in their native language and Q will provide answers, knowledge article links, and recommended step-by-step guides in said language. New languages supported include: Chinese, French, French (Canadian), Italian, Japanese, Korean, Malay, Portuguese, Spanish, Swedish, and Tagalog.
Stability AI’s Stable Diffusion 3.5 Large (SD3.5 Large) is now available in Amazon Bedrock. SD3.5 Large is an advanced text-to-image model featuring 8.1 billion parameters. Trained on Amazon SageMaker HyperPod, this powerful model will enable AWS customers to generate high-quality, 1-megapixel images from text descriptions with superior accuracy and creative control.
The model excels at creating diverse, high-quality images across multiple styles, making it valuable for media, gaming, advertising, ecommerce, corporate training, retail, and education industries. Its enhanced capabilities include exceptional photorealism with detailed 3D imagery, superior handling of multiple subjects in complex scenes, and improved human anatomy rendering. The model also generates representative images with diverse skin tones and features without requiring extensive prompting. Today, Stable Image Ultra in Amazon Bedrock has been updated to include Stable Diffusion 3.5 Large in the model’s underlying architecture.
AWS Partner Central Analytics now provides insights for resell revenue for AWS Partners participating in the AWS Solution Provider or Distribution Programs. The new data helps Partners gain visibility into amortized revenue generated through resellers. With this new resell revenue section in Partner Central Analytics, Partners can measure revenue by month, reseller, end customer, and geography to help define sales growth strategies.
Prior to this launch, Partners could see discounts for authorized resell services, but received limited visibility into program revenue. With this launch, the “Solution provider and distributor“ tab within Partner Central Analytics is renamed to ”Channel“, delivering four new visualizations, providing Partners with a view into amortized resell revenue by program revenue and net program revenue. Key new features include amortized Partner revenue tracking to measure customer impact, monthly refresh schedule to include previous month’s data, and comprehensive coverage of resell revenue on authorized services. This update gives Partners customer level insights, helping Partners understand spending patterns across different regions. This helps Partners decide where and how to invest to expand their market reach. Also, these new insights help Partners better track revenue generated before and after program discounts.
Approved users of an AWS Partner organization at either the Validated or Differentiated stage can access the new datasets through the Analytics tab in AWS Partner Central.
To learn more about resell revenue available in the analytics dashboard, log in to AWS Partner Central and explore the Analytics and Insights User Guide
AWS ParallelCluster 3.12 is now generally available. This release makes it possible to include Lustre and NVIDIA software components in ParallelCluster custom images. Now, you can include ParallelCluster’s recommended Nvidia drivers and CUDA libraries in custom images. This update also makes the Lustre client optional to account for scenarios where you may opt for alternative storage solutions. To enable these optional software components when creating custom images, configure the NvidiaSoftware and the LustreClient parameters in the build image configuration file when using the build-image command.
For more details on the release, review the AWS ParallelCluster 3.12.0 release notes.
AWS ParallelCluster is a fully-supported and maintained open-source cluster management tool that enables R&D customers and their IT administrators to operate high-performance computing (HPC) clusters on AWS. AWS ParallelCluster is designed to automatically and securely provision cloud resources into elastically-scaling HPC clusters capable of running scientific, engineering, and machine-learning (ML/AI) workloads at scale on AWS.
AWS ParallelCluster is available at no additional charge in the AWS Regions listed here, and you pay only for the AWS resources needed to run your applications. To learn more about launching HPC clusters on AWS, visit the AWS ParallelCluster User Guide. To start using ParallelCluster, see the installation instructions for ParallelCluster UI and CLI.
Today, AWS IoT Device Management announces the general availability of a high-throughput connectivity status query API, allowing developers to query the latest connectivity state of IoT devices, for monitoring and management purposes. AWS IoT Device Management is a fully managed cloud service that helps you register, organize, monitor, and remotely manage Internet of Things (IoT) devices at scale.
Device connectivity status is crucial for monitoring device failures and executing remote commands. The new connectivity status API which will be available to AWS IoT Device Management Fleet Indexing customers, provides a high-throughput solution (350+ requests per second) for customers to ascertain device connectivity to the cloud. It also retrieves most recent connect or disconnect event timestamp along with disconnect reason, aiding troubleshooting activities. AWS IoT Device Management’s Fleet Indexing feature enables customers to search, group devices based on device metadata, state stored across thing registry, IoT device shadow and connectivity data sources. While existing search queries are optimized for fleet-level querying, this API is optimized for single-device connectivity queries and offers lower latency to reflect connectivity state changes. With connectivity status queries, developers can now easily support targeted device monitoring and management capabilities in their applications. For example, in automotive applications, developers can first query vehicle connectivity status using this API prior to issuing remote commands to the vehicle.
Connectivity status is available to AWS IoT Device Management Fleet indexing customers and in all AWS regions where AWS IoT Device Management is available. For more information please refer to the developer guide and API documentation.
Amazon RDS Proxy announces caching_sha2_password authentication plugin support for client to proxy connections on MySQL on Aurora and RDS.
Customers need to use plugins to perform authentication between databases and clients while using RDS Proxy. Starting with MySQL 8.4, community MySQL uses caching_sha2_password plugin as the default, which is more secure than the previous default plugins. To align with this, starting today, caching_sha2_password will also be the default authentication plugin for new connection creates with RDS Proxy, if a value is not specified.
RDS Proxy is a fully managed and a highly available database proxy for Amazon Aurora and RDS databases. RDS Proxy helps improve application scalability, resiliency, and security. You can setup your RDS Proxy to use caching_sha2_password authentication on all available RDS for MySQL and Aurora MySQL versions with just a few clicks on the Amazon RDS Management Console or using the AWS SDK or CLI. Support for caching_sha2_password with RDS Proxy is available in all AWS Commercial Regions. For more information, including instructions on getting started, read the Amazon RDS Proxy documentation.
Amazon Connect now supports multi-party chat, allowing up to 4 additional agents to join an ongoing chat conversation, making it easier to collaborate and resolve customer issues quickly. For example, agents can add a supervisor or subject matter experts to join the chat, ensuring customers receive accurate and timely support.
Multi-party chat can be enabled within the AWS Console. Once enabled, agents can simply use a Quick Connect to invite additional agents to an ongoing chat. This feature is available in all commercial AWS regions where Amazon Connect is available. To learn more and get started, please refer to the help documentation or visit the Amazon Connect website.
Meta’s Llama 3.3 70B model is now available in Amazon Bedrock. Llama 3.3 70B represents a significant advancement in model efficiency and performance optimization. This instruction-tuned model delivers impressive capabilities across diverse tasks, including multilingual dialogue, text summarization, and complex reasoning. Llama 3.3 70B is a text-only instruction-tuned model that provides enhanced performance relative to Llama 3.1 70B–and to Llama 3.2 90B when used for text-only applications.
The new model delivers similar performance to Llama 3.1 405B, while requiring only a fraction of the computational resources. Llama 3.3 demonstrates substantial improvements in reasoning, mathematical understanding, general knowledge, and instruction following. Its comprehensive training enables robust language understanding across multiple domains. You can use Llama 3.3 for enterprise applications, content creation, and advanced research initiatives. The model supports multiple languages and outperforms many existing conversational models on industry standard benchmarks. It also supports the ability to leverage model outputs to improve other models including synthetic data generation and distillation. Llama 3.3 provides an accessible and powerful generative AI solution for businesses seeking high-quality, efficient language model capabilities.
Meta’s Llama 3.3 70B model is available in Amazon Bedrock in the US East (Ohio) Region, and in the US East (N. Virginia) and US West (Oregon) Regions via cross-region inference. To learn more, visit the Llama product page and documentation. To get started with Llama 3.3 70B in Amazon Bedrock, visit the Amazon Bedrock console.
Amazon AppStream 2.0 now offers support for Rocky Linux from CIQ, enabling ISVs and central IT organizations to stream from an RPM Package Manager (RPM) compatible environment optimized for running compute-intensive applications while leveraging the flexibility, scalability, and cost-effectiveness of the AWS Cloud. With this launch, customers have the flexibility to choose from a broader set of operating systems including Rocky Linux, Red Hat Enterprise Linux (RHEL), and Microsoft Windows.
This launch enables organizations to stream Rocky Linux apps from AppStream 2.0, helping to accelerate time to market, scaling resources up or down with demand, and managing the entire fleet centrally through the AWS Management Console. Rocky Linux on AppStream 2.0 also enables traditional desktop apps to be converted to SaaS delivery without the cost of refactoring, while pay-as-you-go billing and license-included images ensure you only pay for the resources you use.
Rocky Linux-based AppStream 2.0 instances are supported in all AWS Regions where AppStream 2.0 is available and use per second billing (with a minimum of 15 minutes). For more information, see Amazon AppStream 2.0 pricing.
Amazon Elastic Container Services (Amazon ECS) now allows you to perform network fault injection experiments on your applications deployed on AWS Fargate. Fault injection experiments create disruptions to test how your applications behave, helping you improve application performance, observability, and resilience. AWS Fault Injection Service (AWS FIS) now supports 6 actions for ECS on both EC2 and Fargate: network latency, network blackhole, network packet loss, CPU stress, I/O stress, and kill process.
Developers and operators can now verify the response of their application to potential network errors, some of which may also be required for regulatory compliance. By reproducing network behaviors that may cause applications to fail, you can identify gaps in application configurations, monitoring, alarms, and operational response. Amazon ECS is introducing the ability to opt-in to allow tasks to use a fault injector such as AWS FIS to perform network experiments for increasing network latency, increasing packet loss, and blackhole port testing (dropping inbound or outbound traffic) to test how your applications perform, in addition to existing resource stress experiments.
AWS Config now supports 3 additional AWS resource types. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources.
With this launch, if you have enabled recording for all resource types, then AWS Config will automatically track these new additions. The newly supported resource types are also available across the AWS Config feature set, including Config rules, Config aggregators, and Config advanced queries.
You can now use AWS Config to monitor the following newly supported resource types in all AWS Regions where the supported services are available:
AWS::Cognito::IdentityPool
AWS::MediaConnect::Gateway
AWS::OpenSearchServerless::VpcEndpoint
To view the complete list of AWS Config supported resource types, see supported resource types page.
Amazon AppStream 2.0 extends native client support to macOS. Users can now access AppStream 2.0 streamed applications through a web browser, the AppStream 2.0 client for Windows, or AppStream 2.0 client for macOS. This additional macOS support provides more flexibility and platform options for users needing access to streamed applications and desktops.
The AppStream 2.0 client for macOS is an application you install on your Mac to access AppStream streaming sessions. The client provides enhanced capabilities and user experience. It supports two monitors with 4K resolution, and the ability to connect up to four monitors, leveraging multi-monitor layouts. Users can use keyboard shortcuts and relative mouse mode for a more natural feel.
Using macOS client you can also stream over UDP which offers a more responsive streaming quality in sub-optimal network conditions, with higher round trip latency. Additionally, to assist with troubleshooting macOS client issues, you can enable client automatic logging.
The macOS client works with Windows, Red Hat Enterprise Linux, Rocky Linux 8 based AppStream 2.0 applications and fleets. To run the AppStream 2.0 macOS client, ensure that your Mac is running macOS 13 (Ventura), macOS 14 (Sonoma), or macOS 15 (Sequoia).
To download and install the AppStream 2.0 macOS client application, visit Amazon AppStream 2.0 Downloads, choose the macOS link, download and install the application. Verify that the Amazon AppStream 2.0 client application icon appears on your Mac Launchpad and start streaming.
As the year winds down and we gather around the (digital) fireplace, the excitement around Google Cloud databases is ramping up! It’s been a busy season of innovation and customer success, and we’ve got a sack full of updates to share. Join us for a festive feast of news, including key announcements in the databases space, cool product updates and feature notes, inspiring customer stories, and a calendar of events to ring in the new year. So grab a cup of hot cocoa, settle in, and let’s unwrap the latest happenings from Google Cloud databases.
Key database announcements
[Forrester Wave] Google was named a Leader in The Forrester Wave™: Translytical Data Platforms, Q4 2024 report, earning the highest score possible in 11 criteria, including vision, innovation, gen AI/LLM, real-time analytics, and data security. We believe this recognition solidifies AlloyDB as Google Cloud’s fastest-growing database and a top choice for supporting transactional and analytical workloads with exceptional performance, availability, and scale.
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Advanced DR for PostgreSQL and MySQLnow in GA – Advanced DR, a combination of switchover and replica failover along with a write Eendpoint (in Preview), provides seamless disaster recovery testing and execution without requiring application changes.
Cloud SQL Enterprise Plus for PostgreSQL and MySQL got two new features that improve availability:
Data cache enable/disable– Allows users to enable or disable data cache with near-zero downtime for MySQL and PostgreSQL primary instances.
Instance scaledown – Near-zero downtime is now available for infrequent instance scaledowns on Enterprise Plus for MySQL and PostgreSQL primary instances.
Private Service Connect (PSC) Automation is in Preview – Customers can automate the creation of Cloud SQL PSC endpoints in their VPCs. This dramatically simplifies the deployment of Cloud SQL using PSC especially at scale.
Cloud SQL Studio now supports IAM Authentication for MySQL and PostgreSQL (doc).
AlloyDB for PostgreSQL
Model endpoint management allows customers to access third-party and custom hosted models directly from their database. The extension enabling AI model predictions and embedding generation is now installed by default in new databases. Vertex AI’s latest models are available with no setup.
Single shard clusters – Memorystore for Redis Cluster is now GA. This enables customers to create Memorystore instances with a single shard, and dynamically scale the instance size up or down while maintaining their 99.99% SLA with multi-zone instances having replicas.
Node-level metrics – Memorystore for Redis Cluster is now GA. Node-level metrics empower Memorystore customers with the advanced monitoring capabilities to better manage their clusters.
OSS Autoscaler – Memorystore Cluster now in GA – OSS autoscaler ensures that customer clusters are automatically optimized for performance, capacity, and budget.
Directed reads feature is now GA – Spanner provides the flexibility to route read traffic (except in RW transactions) to a specific replica type or region within a multi-region instance configuration or a custom regional configuration with optional read-only region(s).
Query Optimizer version 7 is now in GA – Spanner’s query optimizer now rewrites OR operators on indexed columns as a UNION MERGE, improving efficiency by avoiding full table scans
Usage statistics and insights dashboard for database splits is now GA – Spanner’s new observability feature helps identify and address performance hotspots by showing how data is distributed across splits (slide).
Firestore Key Access Justifications (KAJ) is now GA – Key access justifications provide customers with more control over data access and the ability to manage key access requests.
Monitoring page on Firestore console allows customers to view available metrics, create a custom dashboard, and set alerts directly from the Firestore console.
Businesses transforming with Google Cloud databases
Discover how Google Cloud database solutions are driving innovation and success:
Ford Pro Intelligence leverages Google Cloud’s Bigtable to harness the power of connected vehicle data, providing real-time insights and predictive maintenance for fleets of all sizes.
Fire up your innovation engine with Spanner: Today’s applications demand more than traditional databases can deliver. In this webinar, we explore Spanner, the ‘always-on’ globally available database that scales effortlessly to power your next-generation innovations.
Maximize performance with Cloud SQL and Memorystore: In this webinar, we’ll show you the combined power of CloudSQL and Memorystore in order to reduce costs, improve efficiency, and deliver exceptional application experiences.
Cloud Wars with Bob Evans In this episode of Cloud Wars, Bobby Brauer, head of geospatial data engineering at Bayer, discusses their innovative use of AlloyDB. Bayer is using AlloyDB to overcome data challenges in agriculture. AlloyDB provides low-latency data processing and high availability, crucial for handling spikes in data flow during harvest season. Learn how Bayer is transforming geospatial data processing to enhance efficiency and drive data-driven decision-making.
Merv Adrian x Apex Fintech | AlloyDB In the rapidly evolving PostgreSQL DBMS landscape, AlloyDB is emerging as a top choice for organizations with demanding workloads. Join Merv Adrian for an interview with Apex Fintech, a leading provider of brokerage and wealth management services, as they share their experience with AlloyDB and how it helps them achieve high availability and robust security.
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